Research question:

  1. Describe the age-mixing pattern in the CTSBS population. Does the age-mixing pattern of HIV positive participants differ from the general population?
  2. At the individual-level are large ranges in partner ages (bridgewidths) associated with HIV status of a participant?
  3. Large bridgewidths may make it possible for participants to acquire HIV from one age group and transmit to the next, but are they also associated with risky sexual behaviours among participants?

Before conducting imputations, I excluded participants who said their sexual preferences were for “both” genders or the same gender (n = 76). I further excluded people who did not identify as black or coloured (n = 7) and people who did not report partners in the previous year (n = 170). Participants who had missing observations on those characteristics were left in the dataset. This left 1074 relationships reported by 647 participants. Of the 647 participants, 185 reported more than one relationship in the previous year. I imputed 50 datasets using the random forest method for continuous and nominal categorical variables and the “polr” method for our ordinal variables.


Figure 1. Age mixing pattern for randomly selected imputed dataset. Linear mixed effects model without hetero skedastic variance explicitly modeled



Figure 2. Extractions of model slopes, intercepts, intercept variance and residual variance for each imputed dataset



Figure 3. Age mixing pattern for those who are HIV positive



Figure 4. Distribution of bridge widths for each imputed dataset, by sex and HIV status



Figure 5. Model coefficients for relationship between HIV and bridge width. Results from negative binomial, generalized additive models with bridge width as the outcome. This is only among participants who reported more than 1 relationship in the previous year. Models adjust for race and age.



Figure 6. Expected bridge widths for different values of age (smooth term), by gender. Each line represents a different imputed dataset.



Figure 7. Effect of bridgewidth on whether a participant had a concurrent relationship in the previous year, stratified by gender. Odds Ratios (ORs) are from generalized additive models. Bridgewidth was treated as a continuous covariate (linear term) in the model. Models are adjusted for age (smooth term) and race.



Figure 8. Effect of HIV on whether a participant had a concurrent relationship in the previous year, stratified by gender. Odds Ratios (ORs) are from generalized additive models. Models are adjusted for bridgewidth (hypothesized mediator, and continuous linear term), age (smooth term) and race. Many confidence intervals and ORs are not showing because there were huge, inflated CI’s that were outside the limits of the y-axis.



Figure 9. Effect of bridgewidth (hypothesized mediator, and continuous linear term) on whether a participant had a concurrent relationship in the previous year, stratified by gender. Odds Ratios (ORs) are from generalized additive models. Models are adjusted for hiv, age (smooth term) and race.



Figure 10. Predicted probabilities of having had a concurrent relationship in the previous year for different values of age (smooth term), by gender. Each line represents a different imputed dataset. These models adjust for bridgewidth and race



Figure 10. Predicted probabilities of having had a concurrent relationship in the previous year for different values of age (smooth term), by gender. Each line represents a different imputed dataset. The models adjust for hiv, bridgewidth (mediator), and race


Figure 11. Effect of bridgewidth (mediator, continuous linear term) on “Always” using a condom in relationship, stratified by gender. Odds Ratios (ORs) are from generalized additive mixed models with a random intercept for the participant. Models are adjusted for age (smooth term) and race.



Figure 12. Effect of bridgewidth (mediator, continuous linear term) on “Always” using a condom in relationship, stratified by gender. Odds Ratios (ORs) are from generalized additive mixed models with a random intercept for the participant. Models are adjusted for hiv status, age (smooth term) and race.



Figure 13. Effect of HIV on “Always” using a condom in relationship, stratified by gender. Odds Ratios (ORs) are from generalized additive mixed models with a random intercept for the participant. Models are adjusted for bridgewidth (mediator, continuous linear term), age and race.



Figure 14. Predicted probabilities of “Always” using a condom in a relationship for different ages of participant, stratified by gender. The predictions are from generalised additive mixed models with a random intercept for the participant. Models are adjusted for bridgewidth (continuous, linear term) and race.



Figure 15. Predicted probabilities of “Always” using a condom in a relationship for different ages of participant, stratified by gender. The predictions are from generalised additive mixed models with a random intercept for the participant. Models are adjusted for bridgewidth (mediator, continuous linear term), hiv status and race.



Figure 16. Distribution of average number of times sex occurred per week in relationships, stratified by gender and imputation dataset



Figure 17. Effect of bridgewidth (continuous linear term) on average number of times sex occurred per week in the relationship, stratified by gender. Incidence Rate Ratios (IRRs) are from generalized additive mixed models (poisson outcome) with a random intercept for the participant. Models are adjusted for age (smooth term) and race.



Figure 18. Effect of HIV on average number of times sex occurred per week in the relationship, stratified by gender. Incidence Rate Ratios (IRRs) are from generalized additive mixed models (poisson outcome) with a random intercept for the participant. Models are adjusted for bridgewidth (continuous linear term), age (smooth term) and race.



Figure 19. Effect of bridgewidth (mediator, continous linear term) on average number of times sex occurred per week in the relationship, stratified by gender. Incidence Rate Ratios (IRRs) are from generalized additive mixed models (poisson outcome) with a random intercept for the participant. Models are adjusted for hiv status, age (smooth term) and race.



Figure 20. Predicted average number of times sex occurred per week in a relationship for different ages of participant, stratified by gender. The predictions are from generalised additive mixed models (poisson outcome) with a random intercept for the participant. Models are adjusted for bridgewidth (continuous linear term) and race.



Figure 21. Predicted average number of times sex occurred per week in a relationship for different ages of participant, stratified by gender. The predictions are from generalised additive mixed models (poisson outcome) with a random intercept for the participant. Models are adjusted for bridgewidth (mediator, continuous linear term), hiv status, and race.